Although Qualcomm has been doing AI research for more than 10 years, a dedicated R&D unit will help to coordinate AI development efforts across the company
In a week where Qualcomm, Microsoft, and Intel are all holding simultaneous artificial intelligence (AI) developer conferences, Qualcomm announced the formation of a dedicated AI research organization aimed at coordinating the company’s R&D efforts. Although the move does not mark Qualcomm’s foray into AI research — it claims to have been working on AI for more than 10 years already — the move should help to focus the company’s efforts on exploring the full spectrum of how AI can be integrated across its product lines.
What took so long?
If Qualcomm has been doing AI research for upwards of a decade, why is it only now deciding to put a formal structure in place to coordinate efforts across the company? While the company certainly could have taken such a step in the past, now remains a good time to put increased structure around its efforts. It is true that AI and machine learning (ML) do hold the potential to change nearly every aspect of the telecom industry, if not society as a whole. However, until now AI and ML have remained somewhat abstract concepts dealing with what is possible, rather than what is achievable today.
With an increased emphasis on network automation that, in turn, is mandating the use of AI and ML to create closed-loop network operations, technology suppliers are more focused than ever on “today” as it relates to incorporating AI and ML technology into their solutions from devices through core network functions. In this case, Qualcomm CTO Jim Thompson summarized the company’s position succinctly in its press release by noting that, “[Qualcomm’s] goal is to make on-device AI ubiquitous.”
The interdependence of ubiquity
Another good reason to coordinate AI research efforts is that as more AI and ML become embedded in devices and networking systems, more unintended consequences of leaving machines to their own proverbial devices will be uncovered. By creating a central AI research unit, Qualcomm helps to put itself in position to ensure that the various AI learnings happening throughout the company are not happening in a vacuum. From an efficiency perspective, this means that the company’s R&D resources should be optimized. Perhaps more importantly, however, it also helps to ensure that breakthroughs are industrialized with a clear view of the downstream implications on all of Qualcomm’s AI-related product sets.
Table of Contents
